Nicolas Heess
Impact in
- Artificial Intelligence top 0.2%
- Reinforcement Learning in Robotics
- Adversarial Robustness in Machine Learning
- Control and Systems Engineering top 0.5%
- Robot Manipulation and Learning
- Traffic control and management
Papers in
-
- Reinforcement Learning in Robotics 34
- Adversarial Robustness in Machine Learning 8
- Evolutionary Algorithms and Applications 4
-
- Generative Adversarial Networks and Image Synthesis 6
- Human Pose and Action Recognition 5
- Advanced Vision and Imaging 5
- Co-authors
- David SilverTom ErezYuval TassaTimothy LillicrapDaan WierstraJonathan J. HuntAlexander PritzelChristopher K. I. Williams
- Journals
- Scientific Reports (1 paper)Physical Review Fluids (1 paper)Journal of Neuroscience (1 paper)ACM Transactions on Graphics (1 paper)Neural Computation (1 paper)
- Partner nations
- United KingdomUnited StatesCanada
In The Last Decade
Nicolas Heess
57 papers receiving 6.3k citations
Hit Papers
Peers
Comparison fields: 5 of 142
- Artificial Intelligence 2.9k
- Control and Systems Engineering 1.8k
- Computer Vision and Pattern Recognition 1.5k
- Automotive Engineering 703
- Computer Networks and Communications 1.1k
Countries citing papers authored by Nicolas Heess
This map shows the geographic impact of Nicolas Heess's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nicolas Heess with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicolas Heess more than expected).
Fields of papers citing papers by Nicolas Heess
This network shows the impact of papers produced by Nicolas Heess. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nicolas Heess. The network helps show where Nicolas Heess may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Nicolas Heess, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2021 | 35 | |
| 3 | A Constrained Multi-Objective Reinforcement Learning Framework | 2021 | 2 |
| 4 | Data-efficient Hindsight Off-policy Option Learning | 2021 | 3 |
| 5 | Value-driven Hindsight Modelling | 2020 | 1 |
| 6 | Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning | 2020 | 17 |
| 7 | RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. | 2020 | 2 |
| 8 | 2020 | 89 | |
| 9 | CoMic: Complementary Task Learning & Mimicry for Reusable Skills | 2020 | 5 |
| 10 | Critic Regularized Regression | 2020 | 1 |
| 11 | V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control | 2020 | 3 |
| 12 | 2019 | 6 | |
| 13 | Distributed Distributional Deterministic Policy Gradients | 2018 | 34 |
| 14 | Learning an Embedding Space for Transferable Robot Skills | 2018 | 66 |
| 15 | Maximum a Posteriori Policy Optimisation | 2018 | 10 |
| 16 | Learning by Playing - Solving Sparse Reward Tasks from Scratch | 2018 | 48 |
| 17 | Imagination-Augmented Agents for Deep Reinforcement Learning | 2017 | 49 |
| 18 | Continuous control with deep reinforcement learning Hit paper breakdown → | 2016 | 4888 |
| 19 | Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection | 2014 | 49 |
| 20 | Searching for objects driven by context | 2012 | 31 |
About Nicolas Heess
Nicolas Heess is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Signal Processing and Cognitive Neuroscience, having authored 58 papers that have together received 6.5k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (34 papers), Robot Manipulation and Learning (10 papers), Adversarial Robustness in Machine Learning (8 papers), Generative Adversarial Networks and Image Synthesis (6 papers), Robotic Locomotion and Control (6 papers), Human Pose and Action Recognition (5 papers), Advanced Vision and Imaging (5 papers) and Evolutionary Algorithms and Applications (4 papers). The work is most often cited by research in Artificial Intelligence (2.9k citations), Control and Systems Engineering (1.8k citations), Computer Vision and Pattern Recognition (1.5k citations), Automotive Engineering (703 citations) and Computer Networks and Communications (1.1k citations). Nicolas Heess has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include David Silver, Tom Erez, Yuval Tassa, Timothy Lillicrap, Daan Wierstra, Jonathan J. Hunt, Alexander Pritzel, Christopher K. I. Williams, John Winn and Geoffrey E. Hinton. Their work appears in journals such as Scientific Reports, Physical Review Fluids, Journal of Neuroscience, ACM Transactions on Graphics and Neural Computation.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.